The Islamic University of Gaza Faculty of Engineering Civil Engineering Department. Numerical Analysis ECIV Chapter 11

Similar documents
SOLVING SYSTEMS OF EQUATIONS, ITERATIVE METHODS

Matrix Eigenvalues and Eigenvectors September 13, 2017

Matrices and Determinants

September 13 Homework Solutions

Engineering Analysis ENG 3420 Fall Dan C. Marinescu Office: HEC 439 B Office hours: Tu-Th 11:00-12:00

INTRODUCTION TO LINEAR ALGEBRA

Chapter 3 MATRIX. In this chapter: 3.1 MATRIX NOTATION AND TERMINOLOGY

Matrices, Moments and Quadrature, cont d

ARITHMETIC OPERATIONS. The real numbers have the following properties: a b c ab ac

Lecture Note 9: Orthogonal Reduction

The Algebra (al-jabr) of Matrices

Numerical Linear Algebra Assignment 008

Chapter 1: Fundamentals

In Section 5.3 we considered initial value problems for the linear second order equation. y.a/ C ˇy 0.a/ D k 1 (13.1.4)

4.5 JACOBI ITERATION FOR FINDING EIGENVALUES OF A REAL SYMMETRIC MATRIX. be a real symmetric matrix. ; (where we choose θ π for.

CHAPTER 4a. ROOTS OF EQUATIONS

THE EXISTENCE-UNIQUENESS THEOREM FOR FIRST-ORDER DIFFERENTIAL EQUATIONS.

Here we study square linear systems and properties of their coefficient matrices as they relate to the solution set of the linear system.

DETERMINANTS. All Mathematical truths are relative and conditional. C.P. STEINMETZ

approaches as n becomes larger and larger. Since e > 1, the graph of the natural exponential function is as below

1 Linear Least Squares

Introduction to Determinants. Remarks. Remarks. The determinant applies in the case of square matrices

Multivariate problems and matrix algebra

Duality # Second iteration for HW problem. Recall our LP example problem we have been working on, in equality form, is given below.

AQA Further Pure 1. Complex Numbers. Section 1: Introduction to Complex Numbers. The number system

LINEAR ALGEBRA AND MATRICES. n ij. is called the main diagonal or principal diagonal of A. A column vector is a matrix that has only one column.

Matrix Algebra. Matrix Addition, Scalar Multiplication and Transposition. Linear Algebra I 24

SCHOOL OF ENGINEERING & BUILT ENVIRONMENT. Mathematics

Energy Bands Energy Bands and Band Gap. Phys463.nb Phenomenon

Module 6: LINEAR TRANSFORMATIONS

Chapter 3 Polynomials

MATRICES AND VECTORS SPACE

Chapter 5 Determinants

A Nonclassical Collocation Method For Solving Two-Point Boundary Value Problems Over Infinite Intervals

Matrix Solution to Linear Equations and Markov Chains

Math 520 Final Exam Topic Outline Sections 1 3 (Xiao/Dumas/Liaw) Spring 2008

How do you know you have SLE?

Homework 5 solutions

Numerical Methods for Chemical Engineers

Lecture 3. In this lecture, we will discuss algorithms for solving systems of linear equations.

Chapter 6 Techniques of Integration

Contents. Outline. Structured Rank Matrices Lecture 2: The theorem Proofs Examples related to structured ranks References. Structure Transport

along the vector 5 a) Find the plane s coordinate after 1 hour. b) Find the plane s coordinate after 2 hours. c) Find the plane s coordinate

Review of Calculus, cont d

CHAPTER 6b. NUMERICAL INTERPOLATION

Chapter 3 Solving Nonlinear Equations

than 1. It means in particular that the function is decreasing and approaching the x-

Autar Kaw Benjamin Rigsby. Transforming Numerical Methods Education for STEM Undergraduates

Chapter 6 Notes, Larson/Hostetler 3e

Chapter 4 Contravariance, Covariance, and Spacetime Diagrams

u t = k 2 u x 2 (1) a n sin nπx sin 2 L e k(nπ/l) t f(x) = sin nπx f(x) sin nπx dx (6) 2 L f(x 0 ) sin nπx 0 2 L sin nπx 0 nπx

Lesson 1: Quadratic Equations

Goals: Determine how to calculate the area described by a function. Define the definite integral. Explore the relationship between the definite

1. Extend QR downwards to meet the x-axis at U(6, 0). y

M344 - ADVANCED ENGINEERING MATHEMATICS

63. Representation of functions as power series Consider a power series. ( 1) n x 2n for all 1 < x < 1

Generalized Fano and non-fano networks

Partial Differential Equations

Mathematics. Area under Curve.

A new algorithm for generating Pythagorean triples 1

Physics 116C Solution of inhomogeneous ordinary differential equations using Green s functions

Math Lecture 23

A-Level Mathematics Transition Task (compulsory for all maths students and all further maths student)

A Matrix Algebra Primer

ODE: Existence and Uniqueness of a Solution

Calculus 2: Integration. Differentiation. Integration

Numerical Methods I Orthogonal Polynomials

CMDA 4604: Intermediate Topics in Mathematical Modeling Lecture 19: Interpolation and Quadrature

Ecuaciones Algebraicas lineales

Chapter 2. Determinants

Modification Adomian Decomposition Method for solving Seventh OrderIntegro-Differential Equations

A Modified ADM for Solving Systems of Linear Fredholm Integral Equations of the Second Kind

5.5 The Substitution Rule

Chapter 1: Logarithmic functions and indices

ITERATIVE SOLUTION REFINEMENT

On the Decomposition Method for System of Linear Fredholm Integral Equations of the Second Kind

Solution to HW 4, Ma 1c Prac 2016

State space systems analysis (continued) Stability. A. Definitions A system is said to be Asymptotically Stable (AS) when it satisfies

arxiv: v2 [math.nt] 2 Feb 2015

Pavel Rytí. November 22, 2011 Discrete Math Seminar - Simon Fraser University

Elementary Linear Algebra

Mathematics Extension 1

Linear Algebra Introduction

Lecture Solution of a System of Linear Equation

LECTURE 10: JACOBI SYMBOL

A - INTRODUCTION AND OVERVIEW

We will see what is meant by standard form very shortly

Polynomials and Division Theory

Elements of Matrix Algebra

1.2. Linear Variable Coefficient Equations. y + b "! = a y + b " Remark: The case b = 0 and a non-constant can be solved with the same idea as above.

DERIVATIVES NOTES HARRIS MATH CAMP Introduction

The Regulated and Riemann Integrals

New Expansion and Infinite Series

Operations with Matrices

Best Approximation. Chapter The General Case

Matrices. Elementary Matrix Theory. Definition of a Matrix. Matrix Elements:

Review of Gaussian Quadrature method

SOLUTION OF SYSTEM OF LINEAR EQUATIONS. Lecture 4: (a) Jacobi's method. method (general). (b) Gauss Seidel method.

Jim Lambers MAT 169 Fall Semester Lecture 4 Notes

2. VECTORS AND MATRICES IN 3 DIMENSIONS

Transcription:

The Islmic University of Gz Fculty of Engineering Civil Engineering Deprtment Numericl Anlysis ECIV 6 Chpter Specil Mtrices nd Guss-Siedel Associte Prof Mzen Abultyef Civil Engineering Deprtment, The Islmic University of Gz

Introduction Certin mtrices hve prticulr structures tht cn be eploited to develop efficient solution schemes A bnded mtri is squre mtri tht hs ll elements equl to zero, with the eception of bnd centered on the min digonl These mtrices typiclly occur in solution of differentil equtions The dimensions of bnded system cn be quntified by two prmeters: the bnd width BW nd hlf-bndwidth HBW These two vlues re relted by BW=HBW+

Bnded mtri

4 Tridigonl Systems A tridigonl system hs bndwidth of : 4 4 4 4 r r r r f e g f e g f e g f An efficient LU decomposition method, clled Thoms lgorithm, cn be used to solve such n eqution The lgorithm consists of three steps: decomposition, forwrd nd bck substitution, nd hs ll the dvntges of LU decomposition

Fig

Cholesky Decomposition This method is suitble for only symmetric systems where: nd A A ij ji A L * L T l l l l l l * l l l l l l T l [ L ] l l l l l 6

Cholesky Decomposition i l l ki ij kj j i ki ki l l ii ij l kj j ki k kk kk l ii kj j k l kk kk l kj j l for i,,, k l for i,,, k l l

Pseudocode for Cholesky s LU Decomposition lgorithm (cont d)

Guss-Siedel Itertive or pproimte methods provide n lterntive to the elimintion methods The Guss- Seidel method is the most commonly used itertive method The system [A]{X}={B} is reshped by solving the first eqution for, the second eqution for, nd the third for, nd n th eqution for n We will limit ourselves to set of equtions

Guss-Siedel b b b b b b Now we cn strt the solution process by choosing guesses for the s A simple wy to obtin initil guesses is to ssume tht they re zero These zeros cn be substituted into eqution to clculte new =b /

Guss-Siedel New is substituted to clculte nd The procedure is repeted until the convergence criterion is stisfied: new old i i i, % s new i

Jcobi itertion Method An lterntive pproch, clled Jcobi itertion, utilizes somewht different technique This technique includes computing set of new s on the bsis of set of old s Thus, s the new vlues re generted, they re not immeditely used but re retined for the net itertion

Guss-Siedel The Guss-Seidel method The Jcobi itertion method

Convergence Criterion for Guss-Seidel Method The guss-siedel method is similr to the technique of fied-point itertion The Guss-Seidel method hs two fundmentl problems s ny itertive method: It is sometimes non-convergent, nd If it converges, converges very slowly Sufficient conditions for convergence of two liner equtions, u(,y) nd v(,y) re: u u y v v y

Convergence Criterion for Guss-Seidel Method (cont d) Similrly, in cse of two simultneous equtions, the Guss-Seidel lgorithm cn be epressed s: b u (, ) b v (, ) u u v v

Convergence Criterion for Guss-Seidel Method (cont d) Substitution into convergence criterion of two liner equtions yield:, In other words, the bsolute vlues of the slopes must be less thn unity for convergence: Tht is, the digonl element must be greter thn the offdigonl element for ech row For n equtions n ii i j ji, j

Guss-Siedel Method- Emple 4 7 7 9 85 7 4 7 9 85 7 7 Guess,, = zero for the first guess Iter, (%), (%), (%) 667-7945 756 99557-49965 79 5 8 76

Improvement of Convergence Using Reltion new new old i i i Where is weighting fctor tht is ssigned vlue between [, ] If = the method is unmodified If is between nd (under reltion) this is employed to mke non convergent system to converge If is between nd (over reltion) this is employed to ccelerte the convergence

Guss-Siedel Method- Emple 8 6 7 4 8 Rerrnge so tht the equtions re digonlly dominnt 8 6 8 7 4 8 8 6 4 7

Guss-Siedel Method- Emple itertion unknown vlue mimum 5 % 766667 % -769 % % 486 88% 855754 % -9476 4% 4% 44659 4% 79968 5% -9999 9% 9%

Guss-Siedel Method- Emple The sme computtion cn be developed with reltion where = new new old First itertion: i i i 8 Reltion yields: 8 8 () 7 6 6 Reltion yields: (7) () Reltion yields: () 8 4 7 5 (5) () 4 () 88 7 4857 88 ( 4857) () 77749

Guss-Siedel Method- Emple Iter unknown vlue reltion mimum 5 % 7 88 % -486-7774 % % 494857 45549 4% 8948 866857 7% -7984-595 85% 85% 9787 7787598 49% 7846745 777757 57% -78-4846 6% 57% 4 46 484655 749% 8695595 889 48% -945-884759 98% 98%